Abstract: Internet of Things (IoT) devices and applications are being deployed in our
homes and workplaces and in our daily lives. These devices often rely on
continuous data collection and machine learning models for analytics and
actuations. However, this approach introduces a number of privacy and
efficiency challenges, as the service operator can perform arbitrary inferences
on the available data. Recently, advances in edge processing have paved the way
for more efficient, and private, data processing at the source for simple tasks
and lighter models, though they remain a challenge for larger, and more
complicated models. In this paper, we present a hybrid approach for breaking
down large, complex deep neural networks for cooperative, privacy-preserving
analytics. To this end, instead of performing the whole operation on the cloud,
we let an IoT device to run the initial layers of the neural network, and then
send the output to the cloud to feed the remaining layers and produce the final
result. We manipulate the model with Siamese fine-tuning and propose a noise
addition mechanism to ensure that the output of the user's device contains no
extra information except what is necessary for the main task, preventing any
secondary inference on the data. We then evaluate the privacy benefits of this
approach based on the information exposed to the cloud service. We also asses
the local inference cost of different layers on a modern handset. Our
evaluations show that by using Siamese fine-tuning and at a small processing
cost, we can greatly reduce the level of unnecessary, potentially sensitive
information in the personal data, and thus achieving the desired trade-off
between utility, privacy, and performance.